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Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19
There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epide...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778620/ https://www.ncbi.nlm.nih.gov/pubmed/36554410 http://dx.doi.org/10.3390/ijerph192416527 |
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author | Gunatilake, Thanushika Miller, Stephen A. |
author_facet | Gunatilake, Thanushika Miller, Stephen A. |
author_sort | Gunatilake, Thanushika |
collection | PubMed |
description | There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter ([Formula: see text]), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios. |
format | Online Article Text |
id | pubmed-9778620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97786202022-12-23 Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 Gunatilake, Thanushika Miller, Stephen A. Int J Environ Res Public Health Article There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter ([Formula: see text]), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios. MDPI 2022-12-09 /pmc/articles/PMC9778620/ /pubmed/36554410 http://dx.doi.org/10.3390/ijerph192416527 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gunatilake, Thanushika Miller, Stephen A. Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title | Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title_full | Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title_fullStr | Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title_full_unstemmed | Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title_short | Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19 |
title_sort | adapting a physical earthquake-aftershock model to simulate the spread of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778620/ https://www.ncbi.nlm.nih.gov/pubmed/36554410 http://dx.doi.org/10.3390/ijerph192416527 |
work_keys_str_mv | AT gunatilakethanushika adaptingaphysicalearthquakeaftershockmodeltosimulatethespreadofcovid19 AT millerstephena adaptingaphysicalearthquakeaftershockmodeltosimulatethespreadofcovid19 |